# A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation

## TL;DR

This paper presents a trie-structured Bayesian model for unsupervised morphological segmentation that integrates neural embeddings and letter successor information, improving segmentation accuracy across multiple languages.

## Contribution

It introduces a novel Bayesian model combining neural embeddings and trie-based priors for unsupervised morphological segmentation.

## Key findings

- Outperforms existing models on Turkish morphology segmentation
- Achieves promising results on English and German with limited resources
- Utilizes neural embeddings and letter successor counts to enhance segmentation accuracy

## Abstract

In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmentation. We adopt prior information from different sources in the model. We use neural word embeddings to discover words that are morphologically derived from each other and thereby that are semantically similar. We use letter successor variety counts obtained from tries that are built by neural word embeddings. Our results show that using different information sources such as neural word embeddings and letter successor variety as prior information improves morphological segmentation in a Bayesian model. Our model outperforms other unsupervised morphological segmentation models on Turkish and gives promising results on English and German for scarce resources.